Abstract
In this study, two commonly used automated methods of detecting cyclones in the lower troposphere were compared with respect to various features of cyclone activity. The first method is based on the neighbor cyclone center point (NCP), while the second method is the cyclone area algorithm (CAA), which relies on the detection of the outermost enclosed contour to identify the horizontal structure of a cyclone. We obtained climatologies of cyclones that affected the Changjiang River–Huaihe River Valleys (CHV) of China (derived from ERA-Interim data for 1979–2015) and compared their structures. We found that the distribution of the track and the cyclogenesis locations of influential cyclones (ICs) showed a consistent spatial pattern between the NCP and CAA. However, there were still notable differences between the statistical features of cyclone activity derived by the NCP and CAA: (1) Only <46% of cyclones shared the same cyclone center between these two schemes. (2) ICs derived from the CAA typically had longer lifetimes and travel distances, with stronger central intensities than those from the NCP. (3) The track of ICs by the CAA with high resolution was consistent with that of ICs by the low-resolution CAA as well as the low-resolution NCP. However, compared to other methods, the high-resolution NCP presented large deviations during the early cyclone stage. The involvement of open systems in the NCP resulted in weaker cyclone intensities and increased uncertainty in cyclone tracking. On the other hand, more cyclones with stronger intensities and longer lifetimes coming from the midlatitudes were detected using the CAA. In addition, the short-lifetime ICs (<18 h) found using the CAA were active (39%) in the CHV, and were typically excluded by the NCP. These ICs had comparable center intensity and showed a good correlation with the occurrence of simultaneous rainfall events.
Highlights
ICsthan between the two algorithms, there were two paths were than but were lower. The reason for this is grid points of the cyclone regime fall in the Changjiang River–Huaihe River Valley (CHV), the cyclone is marked as an influential cyclones (ICs) in the cyclone area algorithm (CAA); whereas more cyclones coming from the northern area
The reason for this is that once the grid points of the cyclone regime fall in the CHV, the cyclone is marked as an IC in the
The CAA requires a closed contour line at this study, 2193 ICs detected in neighbor center point (NCP) did not have apparent closed contours, which contributed to a the outer margin of the ICs to potentially filter out open low systems that are possibly associated with higher frequency of ICs compared to the CAA
Summary
Extratropical cyclones play a fundamental role in transporting meridional heat and energy and are a contributor of air mass redistribution, as well as playing a dominant role in global circulation. 15 international research groups have recently participated in an inter-comparison project (Inter-comparison of Mid-Latitude Storm Diagnostics—IMILAST) to assess the level of uncertainty in different methods, including both the NCP scheme and the CAA [18] They used 15 methods for the automatic identification of cyclones to analyze the similarities and differences in the characteristics of the cyclones revealed by different algorithms, assessing the applicability of the various algorithms [18,24,25,26]. Due to the diversity of the terrain and in particular, the combined effects of the abovementioned two jet streams, cyclones affecting the CHV region are highly diverse, including large-scale cyclones from northern China, low vortices propagating from southwestern China and locally generated cyclones in spring [33] These cyclones, which have different dynamic structures, may increase the uncertainty of automatic identification among different methods. If so, which scheme is preferred for cyclone identification in the CHV? This inter-comparison can shed light on the different physical perspectives that can be obtained by using different cyclone identification approaches
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